Data Mining with Banking CRM to Increase Cross-Selling

The banking sector has witnessed major changes over the last two decades. With an increasing amount of competition and changing customer preferences, banks are focusing on providing quality service to retain market share. CRM solutions play a crucial role in this equation. Banks are seeking to improve their service offerings by providing higher levels of personalization to clients, reengineering sales processes, customizing product portfolios and facilitating the functions of cross-selling and up-selling. To carry out these crucial activities, it is necessary to gain in-depth customer knowledge and take strategic decisions to meet customer needs. Data mining is the tool that can help banks extract useful information from the plethora of data available.

A CRM solution's Cross-Sell Analyzer
Why do banks need data mining?
There are several areas where crucial customer insight can enhance profitability. According to research firm Gartner, one feature that consumers in the banking sector value most today is the online banking facility. However, most financial institutions seem to be failing at providing optimum online services as they do not cater to the requisite customer needs. To do this, it is essential to understand which features the customers are interested in specifically. Banks need to comprehend the needs of customers across different demographic profiles and in relation to different features such as security, online payments and check deposits, ability to analyze spending and cash flows etcetera. This is one example which shows the ardent need for data mining in the banking sector.

Where is the data?
Banks have the most available source of customer data in the form of daily transactions and operations. Banks generate a colossal amount of data through operations such as credit card processing, ATM usage, cash withdrawals and deposits and much more. These are originally designed to support transactions, satisfy audit requirements and/or meet central bank regulations. Banks store such large amounts of this data that it is not humanly possible to sift through it all, but if mined thoroughly, this data can provide valuable information about customers’ attitudes and behavior toward the bank and banking in general. With the use of data mining software, this customer data can be organized and extracted to facilitate management decisions.

The advent of IT systems means that most data is readily available in a format that can be conveniently put through data mining applications. The first step in data mining is to create a veritable and comprehensive data warehouse. This process includes extracting, cleaning, transforming and standardizing data to make it ready for mining and analysis. Data mining begins with a thorough analysis of the data highlighting useful patterns, relationships and associations.

How can data mining help banks?
If used efficiently, data mining combined with an effective CRM solution can provide following benefits -
  • Management can forecast how customers are likely to react to a certain interest rate. 
  • Sales teams can effectively pinpoint customers most likely to purchase a new product. 
  • Banks can also improve customer relations and attain higher profitability by catering to customers’ bespoke needs. 
  • Every client has a unique set of transactions and interactions with the bank. This information can be used to sell them product bundles which fit their precise needs and makes their lives easier. 
  • Products and services can be created or redesigned based on a proper understanding of customer needs. 
In the area of marketing, data mining aids banks to comprehend individual customer preferences and create promotions and offerings to suit these. Additionally, customer insight can help cross-sell products by highlighting the need for such products based on previous usage history. Data mining tools are also extensively used to manage risk in financial institutions by recognizing potential high-risk loan applicants or profitable customers who should be offered new credit cards. Credit scoring is used in this respect to analyze risk before extending loans. A crucial area where data mining is useful is fraud detection. Banks can analyze the usual fraud patterns and cross reference these within their own organization to detect fraudulent activities. Furthermore, banks use data mining for such purposes as customer segmentation, cash management, optimizing stock portfolios and ranking investments. Decision makers can also prevent client attrition by recognizing transaction patterns which are likely to occur right before a client shifts to a competitor.

Banking being a part of the service sector means that a CRM solution is all the more essential for them to maintain their existing customer base and attract prospective clients. Often, all bank employees are not tech-savvy and require palatable information which is easy to access and navigate when interacting with customers. Information generated from data mining can be linked or displayed for pertinent lead and customer records through the CRM solution’s user-friendly interface ensuring users can add value to customer interactions.

Data mining is an essential tool for any banking CRM strategy to be successful. It not only recognizes patterns to make predictions, but can also highlight available opportunities. With the many advantages and new avenues that it offers, this is one tool that no bank can ignore if it wants to retain its customers and stand out in a highly competitive industry. 

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